Predicting Method for Dissolved Gas in Transformer Oil Based on Improved Genetic Algorithm and LS-SVM

WANG Gang1; YANG Hai-tao2; HU Wei-tao3; HUANG Hua-ping4; LI Ning-yuan4

doi:10.13296/j.1001-1609.hva.2010.09.006

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Predicting Method for Dissolved Gas in Transformer Oil Based on Improved Genetic Algorithm and LS-SVM

更新时间：2025-04-26

- Predicting Method for Dissolved Gas in Transformer Oil Based on Improved Genetic Algorithm and LS-SVM
- High VoltageApparatus Vol. 46, Issue 9, Pages: 11-14+18(2010)
- 作者机构：
  
  1. 天津市滨海供电局
  2. 华北电力大学电力系统保护与动态安全监控教育部重点实验室
  3. 四川省广安电业局调通中心
  4. 河北省电力公司超高压输变电分公司
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2010.09.006
  CLC： TM407
- Published：2010
- 稿件说明：
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[1]王罡,杨海涛,胡伟涛,黄华平,李宁远.基于改进遗传算法与LS-SVM的变压器故障气体预测方法[J].高压电器,2010,46(09):11-14+18.

WANG Gang1, YANG Hai-tao2, HU Wei-tao3, et al. Predicting Method for Dissolved Gas in Transformer Oil Based on Improved Genetic Algorithm and LS-SVM[J]. High voltageapparatus, 2010, 46(9): 11-14+18.
[1]王罡,杨海涛,胡伟涛,黄华平,李宁远.基于改进遗传算法与LS-SVM的变压器故障气体预测方法[J].高压电器,2010,46(09):11-14+18. DOI： 10.13296/j.1001-1609.hva.2010.09.006.

WANG Gang1, YANG Hai-tao2, HU Wei-tao3, et al. Predicting Method for Dissolved Gas in Transformer Oil Based on Improved Genetic Algorithm and LS-SVM[J]. High voltageapparatus, 2010, 46(9): 11-14+18. DOI： 10.13296/j.1001-1609.hva.2010.09.006.

摘要

最小二乘支持向量机（LS-SVM）能较好地解决小样本、非线性数据特征的多分类问题

适用于电力变压器油色谱故障气体预测

但参数c与σ2的选取对预测结果影响较大

有必要对其进行优化选择。笔者提出一种基于改进遗传算法（IGA）的参数寻优方法

并将其应用到变压器油中故障气体预测。IGA算法采用了编码机制

随机产生初始种群

可快速扩大搜索空间

稳定群体中个体多样性

有效提高全局搜索能力和收敛速度。最后进行了多组现场数据的实例分析

结果表明:基于IGA进行参数优化后的预测准确率明显优于传统LS-SVM预测结果。

Abstract

LS-SVM（least square support vector machine） is applied to solve the multi-classification problems of small samples and non-linear data

c and σ2 are suitable for predicting dissolved gas in transformer oil.However

the selection of the parameters has clear impacts on the result of prediction

so it is necessary to optimize those parameters.In this paper

a new method to optimize those parameters based on IGA（improved genetic algorithm） is proposed and applied to predict dissolved gas in transformer oil.The IGA uses the encoding mechanism to randomly generates the initial population

rapidly expands the search space

stabilizes the diversity of the individuals in population

and effectively improves the global search ability and convergence speed.Case analyses of some sets of oil chromatogram data demonstrate that the prediction accuracy of the IGA-based LS-SVM is better than that of the conventional LS-SVM model.

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